{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:Using MoXing-v1.14.1-ddfd6c9a\n",
      "INFO:root:Using OBS-Python-SDK-3.1.2\n",
      "INFO:root:Listing OBS: 1000\n",
      "INFO:root:Listing OBS: 2000\n",
      "INFO:root:Listing OBS: 3000\n",
      "INFO:root:Listing OBS: 4000\n",
      "INFO:root:Listing OBS: 5000\n",
      "INFO:root:pid: None.\t1000/5001\n",
      "INFO:root:pid: None.\t2000/5001\n",
      "INFO:root:pid: None.\t3000/5001\n",
      "INFO:root:pid: None.\t4000/5001\n",
      "INFO:root:pid: None.\t5000/5001\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Copy procedure is completed !\n"
     ]
    }
   ],
   "source": [
    "import moxing as mox\n",
    "# mox.file.copy_parallel('s3://obs-aifood-bj4/aifood',\n",
    "# 's3://obs-aifood-huqi88/aifood')\n",
    "mox.file.copy_parallel('s3://obs-aifood-huqi88/aifood','./aifood/')\n",
    "print('Copy procedure is completed !')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "55\n",
      "冰激凌\n",
      "/train/冰激凌\n",
      "/data-for-gen\n",
      "/train/冰激凌\n",
      "当前项目路径：/home/ma-user/work\n",
      "/home/ma-user/work/data-for-gen/train/冰激凌\n",
      "目录新建成功：/home/ma-user/work/data-for-gen/train/冰激凌\n",
      "目录新建成功：/home/ma-user/work/data-for-gen/val/冰激凌\n",
      "i0_glob\n",
      "prepare 冰激凌 images for data augumentation\n",
      "range(0, 500)\n",
      "鸡蛋布丁\n",
      "/train/鸡蛋布丁\n",
      "/data-for-gen\n",
      "/train/鸡蛋布丁\n",
      "当前项目路径：/home/ma-user/work\n",
      "/home/ma-user/work/data-for-gen/train/鸡蛋布丁\n",
      "目录新建成功：/home/ma-user/work/data-for-gen/train/鸡蛋布丁\n",
      "目录新建成功：/home/ma-user/work/data-for-gen/val/鸡蛋布丁\n",
      "i1_glob\n",
      "prepare 鸡蛋布丁 images for data augumentation\n",
      "range(0, 500)\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import subprocess\n",
    "import time\n",
    "from glob import glob\n",
    "f=open(\"./aifood/meta/labels_10c.txt\",\"r\")\n",
    "names = locals()\n",
    "DATA_DIR = '/data-for-gen'\n",
    "# 我们使用1/9的数据作为验证集：\n",
    "val_split = 1/9\n",
    "data_glob = glob('./aifood/images/*/*.jpg')\n",
    "_index = int(len(data_glob)/10 * val_split)\n",
    "print(_index)\n",
    "\n",
    "if os.path.exists(DATA_DIR) == False:\n",
    "    def gen_lnk_cmds(class_name, class_glob):\n",
    "        cmds = ''\n",
    "        print(range(len(class_glob)))\n",
    "        for _i in range(len(class_glob)):\n",
    "            time.sleep(1/20)\n",
    "            filename = os.path.basename(class_glob[_i])\n",
    "\n",
    "            src_path = os.path.realpath(class_glob[_i])\n",
    "            sample_type = 'train' if _i > _index else 'val'\n",
    "            lnk_path = os.path.realpath('./{}/{}/{}/{}'.format(DATA_DIR,sample_type, class_name, filename))\n",
    "#             print(lnk_path)\n",
    "            cmds = cmds + 'ln -s {} {}\\n'.format(src_path, lnk_path)\n",
    "#             print(cmds)\n",
    "\n",
    "        return cmds\n",
    "    for index,i in enumerate(f):\n",
    "        print(i.strip())\n",
    "        name=i.strip()\n",
    "        path = '/train/'+name\n",
    "        valpath = '/val/'+name\n",
    "        print(path)\n",
    "        print(DATA_DIR)\n",
    "        print(path)\n",
    "        # 打印文件目录\n",
    "        print(\"当前项目路径：\" + os.getcwd())\n",
    "\n",
    "        # 获取到当前文件的目录，并检查是否有 directory_time 文件夹，如果不存在则自动新建 directory_time 文件\n",
    "        try:\n",
    "            File_Path =   os.getcwd() + DATA_DIR +  path \n",
    "            Val_Path =  os.getcwd() + DATA_DIR +  valpath \n",
    "            print(File_Path)\n",
    "            # 判断是否已经存在该目录\n",
    "            if not os.path.exists(File_Path):\n",
    "                # 目录不存在，进行创建操作\n",
    "                os.makedirs(File_Path) #使用os.makedirs()方法创建多层目录\n",
    "                print(\"目录新建成功：\" + File_Path)\n",
    "            else:\n",
    "                print(\"目录已存在！！！\")\n",
    "            if not os.path.exists(Val_Path):\n",
    "                # 目录不存在，进行创建操作\n",
    "                os.makedirs(Val_Path) #使用os.makedirs()方法创建多层目录\n",
    "                print(\"目录新建成功：\" + Val_Path)\n",
    "            else:\n",
    "                print(\"目录已存在！！！\")\n",
    "        except BaseException as msg:\n",
    "            print(\"新建目录失败：\" + msg)\n",
    "\n",
    "        names['i' + str(i)+'_glob'] = glob('./aifood/images/'+name+'/*.jpg')\n",
    "        print('i' + str(index) +'_glob') \n",
    "        \n",
    "        print('prepare {} images for data augumentation'.format(name))\n",
    "        with open('./tmp_gen_i{}.sh'.format(index), 'w') as f:\n",
    "#             print(names.get('i' + str(i)+'_glob'))\n",
    "            link_cmds = gen_lnk_cmds(name, names.get('i' + str(i)+'_glob'))\n",
    "           \n",
    "            f.write(link_cmds)\n",
    "            time.sleep(1)\n",
    "            subprocess.run(['sh', './tmp_gen_i{}.sh'.format(index)])\n",
    "            time.sleep(1)\n",
    "            subprocess.run(['rm', './tmp_gen_i{}.sh'.format(index)])\n",
    "        \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./data-for-gen/train/三明治/44006.jpg\n",
      "./data-for-gen/train/三明治/44095.jpg\n",
      "./data-for-gen/train/三明治/43865.jpg\n",
      "./data-for-gen/train/三明治/44035.jpg\n",
      "./data-for-gen/train/三明治/44075.jpg\n",
      "./data-for-gen/train/三明治/43706.jpg\n",
      "./data-for-gen/train/三明治/44022.jpg\n",
      "./data-for-gen/train/三明治/43723.jpg\n",
      "./data-for-gen/train/三明治/44083.jpg\n",
      "./data-for-gen/train/三明治/44091.jpg\n",
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      "./data-for-gen/train/烤冷面/41789.jpg\n",
      "./data-for-gen/train/烤冷面/41757.jpg\n",
      "./data-for-gen/train/烤冷面/41671.jpg\n",
      "./data-for-gen/train/烤冷面/41508.jpg\n",
      "./data-for-gen/train/烤冷面/41616.jpg\n",
      "./data-for-gen/train/烤冷面/41888.jpg\n",
      "./data-for-gen/train/烤冷面/41693.jpg\n",
      "./data-for-gen/train/烤冷面/41860.jpg\n",
      "./data-for-gen/train/烤冷面/41710.jpg\n",
      "./data-for-gen/train/烤冷面/41869.jpg\n",
      "./data-for-gen/train/烤冷面/41570.jpg\n",
      "./data-for-gen/train/烤冷面/41894.jpg\n",
      "./data-for-gen/train/烤冷面/41829.jpg\n",
      "./data-for-gen/train/烤冷面/41563.jpg\n",
      "./data-for-gen/train/烤冷面/41787.jpg\n",
      "./data-for-gen/train/烤冷面/41591.jpg\n",
      "./data-for-gen/train/烤冷面/41497.jpg\n",
      "./data-for-gen/train/烤冷面/41818.jpg\n",
      "./data-for-gen/train/烤冷面/41514.jpg\n",
      "./data-for-gen/train/烤冷面/41969.jpg\n",
      "./data-for-gen/train/烤冷面/41881.jpg\n",
      "./data-for-gen/train/烤冷面/41723.jpg\n",
      "./data-for-gen/train/烤冷面/41567.jpg\n",
      "./data-for-gen/train/烤冷面/41953.jpg\n",
      "./data-for-gen/train/烤冷面/41499.jpg\n",
      "./data-for-gen/train/烤冷面/41647.jpg\n",
      "./data-for-gen/train/烤冷面/41573.jpg\n",
      "./data-for-gen/train/烤冷面/41564.jpg\n",
      "./data-for-gen/train/烤冷面/41938.jpg\n",
      "./data-for-gen/train/烤冷面/41955.jpg\n",
      "./data-for-gen/train/烤冷面/41494.jpg\n",
      "./data-for-gen/train/烤冷面/41793.jpg\n",
      "./data-for-gen/train/烤冷面/41686.jpg\n",
      "./data-for-gen/train/烤冷面/41504.jpg\n",
      "./data-for-gen/train/烤冷面/41917.jpg\n",
      "./data-for-gen/train/烤冷面/41510.jpg\n",
      "./data-for-gen/train/烤冷面/41480.jpg\n",
      "./data-for-gen/train/烤冷面/41624.jpg\n",
      "./data-for-gen/train/烤冷面/41536.jpg\n",
      "./data-for-gen/train/烤冷面/41821.jpg\n",
      "./data-for-gen/train/烤冷面/41696.jpg\n",
      "./data-for-gen/train/烤冷面/41839.jpg\n",
      "./data-for-gen/train/烤冷面/41944.jpg\n",
      "./data-for-gen/train/烤冷面/41897.jpg\n",
      "./data-for-gen/train/烤冷面/41634.jpg\n",
      "./data-for-gen/train/烤冷面/41650.jpg\n",
      "./data-for-gen/train/烤冷面/41515.jpg\n",
      "./data-for-gen/train/烤冷面/41775.jpg\n",
      "./data-for-gen/train/烤冷面/41522.jpg\n",
      "./data-for-gen/train/烤冷面/41849.jpg\n",
      "./data-for-gen/train/烤冷面/41727.jpg\n",
      "./data-for-gen/train/烤冷面/41836.jpg\n",
      "./data-for-gen/train/烤冷面/41781.jpg\n",
      "./data-for-gen/train/烤冷面/41529.jpg\n",
      "./data-for-gen/train/烤冷面/41589.jpg\n",
      "./data-for-gen/train/烤冷面/41764.jpg\n",
      "./data-for-gen/train/烤冷面/41769.jpg\n",
      "./data-for-gen/train/烤冷面/41845.jpg\n",
      "./data-for-gen/train/烤冷面/41962.jpg\n",
      "./data-for-gen/train/烤冷面/41898.jpg\n",
      "./data-for-gen/train/烤冷面/41770.jpg\n",
      "./data-for-gen/train/烤冷面/41648.jpg\n",
      "./data-for-gen/train/烤冷面/41875.jpg\n",
      "./data-for-gen/train/烤冷面/41683.jpg\n",
      "./data-for-gen/train/烤冷面/41912.jpg\n",
      "./data-for-gen/train/烤冷面/41580.jpg\n",
      "./data-for-gen/train/烤冷面/41856.jpg\n",
      "./data-for-gen/train/烤冷面/41831.jpg\n",
      "./data-for-gen/train/烤冷面/41745.jpg\n",
      "./data-for-gen/train/烤冷面/41597.jpg\n",
      "./data-for-gen/train/烤冷面/41896.jpg\n",
      "./data-for-gen/train/烤冷面/41826.jpg\n",
      "./data-for-gen/train/烤冷面/41824.jpg\n",
      "./data-for-gen/train/烤冷面/41640.jpg\n",
      "./data-for-gen/train/烤冷面/41476.jpg\n",
      "./data-for-gen/train/烤冷面/41475.jpg\n",
      "./data-for-gen/train/烤冷面/41701.jpg\n",
      "./data-for-gen/train/烤冷面/41804.jpg\n",
      "./data-for-gen/train/烤冷面/41526.jpg\n",
      "./data-for-gen/train/烤冷面/41887.jpg\n",
      "./data-for-gen/train/烤冷面/41559.jpg\n",
      "./data-for-gen/train/烤冷面/41930.jpg\n",
      "./data-for-gen/train/烤冷面/41952.jpg\n",
      "./data-for-gen/train/烤冷面/41588.jpg\n",
      "./data-for-gen/train/烤冷面/41890.jpg\n",
      "./data-for-gen/train/烤冷面/41730.jpg\n",
      "./data-for-gen/train/烤冷面/41485.jpg\n",
      "./data-for-gen/train/烤冷面/41743.jpg\n",
      "./data-for-gen/train/烤冷面/41566.jpg\n",
      "./data-for-gen/train/烤冷面/41809.jpg\n",
      "./data-for-gen/train/烤冷面/41801.jpg\n",
      "./data-for-gen/train/烤冷面/41750.jpg\n",
      "./data-for-gen/train/烤冷面/41676.jpg\n",
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      "./data-for-gen/train/烤冷面/41803.jpg\n",
      "./data-for-gen/train/烤冷面/41731.jpg\n",
      "./data-for-gen/train/烤冷面/41610.jpg\n",
      "./data-for-gen/train/烤冷面/41503.jpg\n",
      "./data-for-gen/train/烤冷面/41936.jpg\n",
      "./data-for-gen/train/烤冷面/41565.jpg\n",
      "./data-for-gen/train/烤冷面/41500.jpg\n",
      "./data-for-gen/train/烤冷面/41557.jpg\n",
      "./data-for-gen/train/烤冷面/41483.jpg\n",
      "./data-for-gen/train/烤冷面/41751.jpg\n",
      "./data-for-gen/train/烤冷面/41556.jpg\n",
      "./data-for-gen/train/烤冷面/41728.jpg\n",
      "./data-for-gen/train/烤冷面/41880.jpg\n",
      "./data-for-gen/train/烤冷面/41923.jpg\n",
      "./data-for-gen/train/烤冷面/41555.jpg\n",
      "./data-for-gen/train/烤冷面/41922.jpg\n",
      "./data-for-gen/train/烤冷面/41619.jpg\n",
      "./data-for-gen/train/烤冷面/41935.jpg\n",
      "./data-for-gen/train/烤冷面/41490.jpg\n",
      "./data-for-gen/train/烤冷面/41883.jpg\n",
      "./data-for-gen/train/烤冷面/41827.jpg\n",
      "./data-for-gen/train/烤冷面/41501.jpg\n",
      "./data-for-gen/train/烤冷面/41711.jpg\n",
      "./data-for-gen/train/烤冷面/41587.jpg\n",
      "./data-for-gen/train/烤冷面/41506.jpg\n",
      "./data-for-gen/train/烤冷面/41511.jpg\n",
      "./data-for-gen/train/烤冷面/41532.jpg\n",
      "./data-for-gen/train/烤冷面/41798.jpg\n"
     ]
    }
   ],
   "source": [
    "from PIL import Image\n",
    "import os \n",
    "import glob \n",
    "\n",
    "\n",
    "def get_image_paths(folder): \n",
    "    return glob.glob(os.path.join(folder, '*/*.jpg')) \n",
    "\n",
    "def create_read_img(filename): \n",
    "    #读取图像\n",
    "    im = Image.open(filename)\n",
    "    im_resized = im.resize((128, 128))\n",
    "    out_h = im.transpose(Image.FLIP_LEFT_RIGHT)\n",
    "    out_w = im.transpose(Image.FLIP_TOP_BOTTOM)\n",
    "    out_90 = im.transpose(Image.ROTATE_90)\n",
    "    out_180 = im.transpose(Image.ROTATE_180)\n",
    "    out_270 = im.transpose(Image.ROTATE_270)\n",
    "    out_resize = im_resized\n",
    "    \n",
    "    out_h.save(filename[:-4]+'_h.jpg')\n",
    "    out_w.save(filename[:-4]+'_w.jpg')\n",
    "    out_90.save(filename[:-4]+'_90.jpg')\n",
    "    out_180.save(filename[:-4]+'_180.jpg')\n",
    "    out_270.save(filename[:-4]+'_270.jpg')\n",
    "    out_resize.save(filename[:-4]+'_128.jpg')\n",
    "    print(filename)\n",
    "    \n",
    "img_path = './data-for-gen/train/' \n",
    "imgs = get_image_paths(img_path) \n",
    "\n",
    "for i in imgs: \n",
    "    create_read_img(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "from __future__ import print_function, division\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from torch.optim import lr_scheduler\n",
    "from torch.autograd import Variable\n",
    "import torchvision\n",
    "from torchvision import datasets, models, transforms\n",
    "import time\n",
    "import os\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'train': 31080, 'val': 560}\n"
     ]
    }
   ],
   "source": [
    "dataTrans = transforms.Compose([\n",
    "            transforms.Resize(256),\n",
    "            transforms.CenterCrop(224),\n",
    "            transforms.ToTensor(),\n",
    "            transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])\n",
    "        ])\n",
    " \n",
    "    # image data path\n",
    "# data_dir = './aifood/images'\n",
    "# all_image_datasets = datasets.ImageFolder(data_dir, dataTrans)\n",
    "# #print(all_image_datasets.class_to_idx)    \n",
    "# trainsize = int(0.8*len(all_image_datasets))\n",
    "# testsize = len(all_image_datasets) - trainsize\n",
    "# train_dataset, test_dataset = torch.utils.data.random_split(all_image_datasets,[trainsize,testsize])\n",
    "\n",
    "train_dir = './data-for-gen/train'\n",
    "train_dataset = datasets.ImageFolder(train_dir, dataTrans)\n",
    "test_dir = './data-for-gen/val'\n",
    "test_dataset = datasets.ImageFolder(test_dir, dataTrans)\n",
    "image_datasets = {'train':train_dataset,'val':test_dataset}\n",
    "    \n",
    "\n",
    "    # wrap your data and label into Tensor\n",
    "\n",
    "    \n",
    "dataloders = {x: torch.utils.data.DataLoader(image_datasets[x],\n",
    "                                                 batch_size=64,\n",
    "                                                 shuffle=True,\n",
    "                                                 num_workers=4) for x in ['train', 'val']}\n",
    "\n",
    "dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']}\n",
    "print(dataset_sizes)\n",
    "    # use gpu or not\n",
    "use_gpu = torch.cuda.is_available()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_model(model, lossfunc, optimizer, scheduler, num_epochs=25):\n",
    "    start_time = time.time()\n",
    "\n",
    "    best_model_wts = model.state_dict()\n",
    "    best_acc = 0.0\n",
    "    train_acc = []\n",
    "    valid_acc = []\n",
    "\n",
    "    for epoch in range(num_epochs):\n",
    "        print('Epoch {}/{}'.format(epoch, num_epochs - 1))\n",
    "        print('-' * 10)\n",
    "\n",
    "        # Each epoch has a training and validation phase\n",
    "        for phase in ['train', 'val']:\n",
    "            if phase == 'train':\n",
    "                scheduler.step()\n",
    "                model.train(True)  # Set model to training mode\n",
    "            else:\n",
    "                model.train(False)  # Set model to evaluate mode\n",
    "\n",
    "            running_loss = 0.0\n",
    "            running_corrects = 0.0\n",
    "\n",
    "            # Iterate over data.\n",
    "            for data in dataloders[phase]:\n",
    "                # get the inputs\n",
    "                inputs, labels = data\n",
    "                \n",
    "\n",
    "                # wrap them in Variable\n",
    "                if use_gpu:\n",
    "                    inputs = Variable(inputs.cuda())\n",
    "                    labels = Variable(labels.cuda())\n",
    "                else:\n",
    "                    inputs, labels = Variable(inputs), Variable(labels)\n",
    "\n",
    "                # zero the parameter gradients\n",
    "                optimizer.zero_grad()\n",
    "\n",
    "                # forward\n",
    "                outputs = model(inputs)\n",
    "                _, preds = torch.max(outputs.data, 1)\n",
    "                loss = lossfunc(outputs, labels)\n",
    "\n",
    "                # backward + optimize only if in training phase\n",
    "                if phase == 'train':\n",
    "                    loss.backward()\n",
    "                    optimizer.step()\n",
    "\n",
    "                # statistics\n",
    "                running_loss += loss.data\n",
    "                running_corrects += torch.sum(preds == labels.data).to(torch.float32)\n",
    "\n",
    "            epoch_loss = running_loss / dataset_sizes[phase]\n",
    "            epoch_acc = running_corrects / dataset_sizes[phase]\n",
    "\n",
    "            print('{} Loss: {:.4f} Acc: {:.4f}'.format(\n",
    "                phase, epoch_loss, epoch_acc))\n",
    "\n",
    "            # deep copy the model\n",
    "            if phase == 'val' and epoch_acc > best_acc:\n",
    "                best_acc = epoch_acc\n",
    "                best_model_wts = model.state_dict()\n",
    "            if phase == 'val':\n",
    "                valid_acc.append(epoch_acc)\n",
    "            else:\n",
    "                train_acc.append(epoch_acc)\n",
    "#         scheduler.step(valid_acc[-1])\n",
    "    elapsed_time = time.time() - start_time\n",
    "    print('Training complete in {:.0f}m {:.0f}s'.format(\n",
    "        elapsed_time // 60, elapsed_time % 60))\n",
    "    print('Best val Acc: {:4f}'.format(best_acc))\n",
    "\n",
    "    # load best model weights\n",
    "    model.load_state_dict(best_model_wts)\n",
    "  \n",
    "    return model,train_acc,valid_acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading: \"https://download.pytorch.org/models/resnet50-19c8e357.pth\" to /home/ma-user/.torch/models/resnet50-19c8e357.pth\n",
      "100%|██████████| 102502400/102502400 [00:08<00:00, 12215587.28it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0/19\n",
      "----------\n",
      "train Loss: 0.0078 Acc: 0.8579\n",
      "val Loss: 0.0028 Acc: 0.9375\n",
      "Epoch 1/19\n",
      "----------\n",
      "train Loss: 0.0016 Acc: 0.9720\n",
      "val Loss: 0.0023 Acc: 0.9571\n",
      "Epoch 2/19\n",
      "----------\n",
      "train Loss: 0.0006 Acc: 0.9921\n",
      "val Loss: 0.0020 Acc: 0.9607\n",
      "Epoch 3/19\n",
      "----------\n",
      "train Loss: 0.0003 Acc: 0.9971\n",
      "val Loss: 0.0020 Acc: 0.9643\n",
      "Epoch 4/19\n",
      "----------\n",
      "train Loss: 0.0002 Acc: 0.9989\n",
      "val Loss: 0.0021 Acc: 0.9571\n",
      "Epoch 5/19\n",
      "----------\n",
      "train Loss: 0.0001 Acc: 0.9991\n",
      "val Loss: 0.0022 Acc: 0.9589\n",
      "Epoch 6/19\n",
      "----------\n",
      "train Loss: 0.0001 Acc: 0.9990\n",
      "val Loss: 0.0021 Acc: 0.9589\n",
      "Epoch 7/19\n",
      "----------\n",
      "train Loss: 0.0001 Acc: 0.9995\n",
      "val Loss: 0.0019 Acc: 0.9643\n",
      "Epoch 8/19\n",
      "----------\n",
      "train Loss: 0.0001 Acc: 0.9998\n",
      "val Loss: 0.0022 Acc: 0.9571\n",
      "Epoch 9/19\n",
      "----------\n",
      "train Loss: 0.0001 Acc: 0.9996\n",
      "val Loss: 0.0023 Acc: 0.9625\n",
      "Epoch 10/19\n",
      "----------\n",
      "train Loss: 0.0001 Acc: 0.9997\n",
      "val Loss: 0.0021 Acc: 0.9571\n",
      "Epoch 11/19\n",
      "----------\n",
      "train Loss: 0.0001 Acc: 0.9999\n",
      "val Loss: 0.0021 Acc: 0.9625\n",
      "Epoch 12/19\n",
      "----------\n",
      "train Loss: 0.0001 Acc: 0.9997\n",
      "val Loss: 0.0021 Acc: 0.9589\n",
      "Epoch 13/19\n",
      "----------\n",
      "train Loss: 0.0001 Acc: 0.9998\n",
      "val Loss: 0.0022 Acc: 0.9589\n",
      "Epoch 14/19\n",
      "----------\n",
      "train Loss: 0.0001 Acc: 0.9995\n",
      "val Loss: 0.0020 Acc: 0.9625\n",
      "Epoch 15/19\n",
      "----------\n",
      "train Loss: 0.0001 Acc: 0.9997\n",
      "val Loss: 0.0020 Acc: 0.9589\n",
      "Epoch 16/19\n",
      "----------\n",
      "train Loss: 0.0001 Acc: 0.9999\n",
      "val Loss: 0.0019 Acc: 0.9625\n",
      "Epoch 17/19\n",
      "----------\n",
      "train Loss: 0.0001 Acc: 0.9997\n",
      "val Loss: 0.0020 Acc: 0.9607\n",
      "Epoch 18/19\n",
      "----------\n",
      "train Loss: 0.0001 Acc: 0.9998\n",
      "val Loss: 0.0021 Acc: 0.9589\n",
      "Epoch 19/19\n",
      "----------\n",
      "train Loss: 0.0001 Acc: 0.9999\n",
      "val Loss: 0.0021 Acc: 0.9607\n",
      "Training complete in 37m 27s\n",
      "Best val Acc: 0.964286\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# get model and replace the original fc layer with your fc layer\n",
    "model_ft = models.resnet50(pretrained=True)\n",
    "# model_ft = models.resnet50(pretrained=False)\n",
    "# model_ft.load_state_dict(torch.load('./pretrained_models/resnet50-19c8e357.pth'))\n",
    "num_ftrs = model_ft.fc.in_features\n",
    "model_ft.fc = nn.Linear(num_ftrs, 10)\n",
    "\n",
    "if use_gpu:\n",
    "    model_ft = model_ft.cuda()\n",
    "\n",
    "    # define loss function\n",
    "lossfunc = nn.CrossEntropyLoss()\n",
    "\n",
    "    # setting optimizer and trainable parameters\n",
    " #   params = model_ft.parameters()\n",
    " # list(model_ft.fc.parameters())+list(model_ft.layer4.parameters())\n",
    "#params = list(model_ft.fc.parameters())+list( model_ft.parameters())\n",
    "params = list(model_ft.fc.parameters())\n",
    "optimizer_ft = optim.SGD(model_ft.parameters(), lr=0.001, momentum=0.9)\n",
    "\n",
    "# LR（学习速率）每7个epoch下降0.1\n",
    "exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=7, gamma=0.1)\n",
    "model_ft,train_acc,valid_acc = train_model(model=model_ft,\n",
    "                           lossfunc=lossfunc,\n",
    "                           optimizer=optimizer_ft,\n",
    "                           scheduler=exp_lr_scheduler,\n",
    "                           num_epochs=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pylab as plt\n",
    "plt.plot(train_acc,label=\"train\")\n",
    "plt.plot(valid_acc,label='valid')\n",
    "plt.legend()\n",
    "plt.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "done\n"
     ]
    }
   ],
   "source": [
    "torch.save(model_ft.state_dict(), './model.pth')\n",
    "print(\"done\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import moxing as mox\n",
    "mox.file.copy('./model.pth','s3://obs-aifood-huqi88/model_output/model/resnet-50.pth')\n",
    "print(\"done\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Pytorch-1.0.0",
   "language": "python",
   "name": "pytorch-1.0.0"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
